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Retraction Note: Tobacco-smoking-related prevalence of methanogens in the oral fluid microbiota

Layout optimization of multi-level cold chain storage facilities in agricultural producing areas considering type and capacity constraints

The effective circulation of fresh agricultural products is conducive to increasing farmers’ income and improving the living standards of urban residents. Cold chain storage facilities in agricultural producing areas play an important role in ensuring the quality of agricultural products, extending the freshness period of goods, and improving logistics efficiency. Different types of fresh produce have different requirements for refrigeration and often require transshipment due to quantity constraints. In addition, there are economies of scale in the construction and operation of cold chain storage facilities. Based on the above considerations, with the aim of minimizing the total daily cost, an optimization model for the layout of multi-level cold chain storage facilities is established to determine the number, location, type and capacity of cold chain storage facilities at the same time. Genetic algorithm is chosen to solve the model according to the characteristics of the model. Taking J County of China as an example, the model is proved to have strong operability and applicability. It is of guiding significance and reference value to optimize the layout of cold chain storage facilities in rural areas.

Comparative determination of factors affecting attitude level towards healthy nutrition

Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops

Using chemical laboratory procedures to estimate the fruit quality parameters (biochemical parameters) of mango "Succarri" and strawberry "Florida" as indicators of ripening degrees in a large area presents challenges such as low throughput, labor intensity, time consumption, and the need for multiple samples. So, using spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits is important to find the best time to harvest, make food ripen faster, and the processing of food easier. This has significant economic and ecological advantages. The objective of this study was to evaluate the biochemical parameters of mango and strawberry fruits at various ripening stages. This was done by utilizing a combination of established and newly developed spectral reflectance indices (SRIs) in conjunction with machine learning (ML) models, including artificial neural networks (ANN), random forests (RF), and decision trees (DT). For mango fruit, the parameters estimated were chlorophyll content, total soluble solids (TSS), and firmness, whereas for strawberry fruit, the parameters were L*, b*, TSS, and firmness. These results revealed significant differences in SRI values across various ripening stages, indicating variances in the fruit’s biochemical parameters. The newly developed SRIs showed superior efficacy in evaluating these parameters. The integration of SRIs with diverse ML models proved to be a successful strategy for precisely estimating biochemical parameters. For mango’s biochemical parameter prediction, the ANN models demonstrated R2 values ranging from 0.92 to 1.00 and from 0.93 to 0.98 for training and testing, respectively. On the other hand, the RF models exhibited R2 values ranging from 0.98 to 1.00 and from 0.93 to 0.99 during training and testing, respectively. The DT models showed high performance, with R2 values ranging from 0.95 to 1.00 and from 0.88 to 0.99 for the training and testing phases. For strawberry’s biochemical parameter prediction, the ANN models achieved R2 values between 0.75 and 0.91 and between 0.58 and 0.91 during training and testing phases, respectively. On the other hand, RF models showed R2 values between 0.85 and 0.91 during training and between 0.74 and 0.86 during testing. The DT models demonstrated excellent results, with R2 values ranging from 0.75 to 0.91 for the training set and 0.74 to 0.81 for the testing set. It can be concluded that combining SRIs with ML models, such as ANN, RF, and DT, can accurately predict the biochemical properties of mango and strawberry fruits.

Drug-resistant epilepsy associated with peripheral complement decreases and sex-specific cytokine imbalances: a pilot study

Bioactivity assessment of peptides derived from salted jellyfish (Rhopilema hispidum) byproducts

The identification of multifunctional peptides derived from marine byproducts represents a significant challenge in the field. In Thailand, the fisheries industry exports salted jellyfish, which results in low-value byproducts primarily employed for animal feed. Previous studies have indicated the bioactivities of jellyfish protein hydrolysates from Lobonema smitthii; however, the multifunctional properties of Rhopilema hispidum remain largely unexplored. This research aimed to characterize synthetic bioactive peptides sourced from the byproducts of salted jellyfish (R. hispidum), with a specific emphasis on their antioxidant, angiotensin-I-converting enzyme (ACE) inhibitory, and anti-inflammatory activities. The hydrolysate obtained from the umbrella portion, subjected to pepsin treatment at a 3:20 enzyme-to-substrate ratio for 48 h at 37 °C, demonstrated the highest levels of antioxidant activity (DPPH =  1.85 ± 0.05 mM TE/mg protein, ABTS =  7.28 ± 0.03 mM TE/mg protein, FRAP =  3.04 ± 0.12 mM FeSO4/mg protein). Following purification, 18 novel peptides exhibiting high antioxidant scores (FRS+CHEL >  0.48) were identified and synthesized. Notably, the peptide MVVACVLPEA exhibited significant antioxidant (DPPH =  56.07 mM TE/mg protein), ACE inhibitory (91.69%), and anti-inflammatory activities (NO release =  34.59 µ M) without cytotoxic effects, although it is important to note that two other peptides did demonstrate cytotoxicity. This investigation reports a total of 16 synthesized peptides that possess triple functional activities—antioxidant, ACE inhibitory, and anti-inflammatory—without cytotoxicity, thus highlighting their potential applications in health-related fields.

Effect and mechanism of coal desulfurization using a surfactant-assisted NaClO-NaOH system

Detection of hepatitis B virus mRNA from single cell RNA sequencing data without prior knowledge

The ability to detect microbial reads from sequencing data has significantly advanced microbiome and infectious disease research. Recently, INVADEseq introduced a technique to extract microbial reads from single-cell RNA sequencing (scRNA-seq) data following 16S rRNA amplification. We hypothesized that this approach could be leveraged to detect viruses in eukaryotic cells without such amplification or prior knowledge, provided they produce viral mRNAs containing poly-A tails. To test this, we aimed to detect Hepatitis B Virus (HBV) reads from liver samples of patients with chronic HBV infection, both with and without HBsAg loss. We successfully detected HBV reads in the liver of viraemic patients, predominantly in hepatocytes and, to a lesser extent, in Kupffer cells. Functionally cured HBV patients with HBsAg loss had undetectable HBV mRNA in the liver. This study demonstrates the ability to extract and identify viral reads from scRNA-seq data without prior knowledge and without specific amplification. This approach can be used for screening scRNA-seq data for the presence of viral reads at single-cell resolution, potentially enhancing our understanding of the cellular distribution of viruses and virus-host interactions.

The protective effect of sodium-glucose cotransporter-2 inhibitor on left ventricular global longitudinal strain in patients with type 2 diabetes mellitus according to disease duration

Individual differences in associative/semantic priming: Spreading of activation in semantic memory and epistemically unwarranted beliefs

Starting from the enhanced spreading of activation through semantic memory (one of the explanatory mechanisms attempting to explain some manifestations observed in schizophrenia) and the psychosis continuum (a dimensional approach to psychotic disorders, where ‘normality’ and ‘psychopathology’ are not qualitatively different in nature but placed on varying levels of the same continuum), the main aim of the present research was to explore whether there are individual differences in associative/semantic priming in people with different levels of epistemically unwarranted beliefs (EUB). Participants varying in paranormal, pseudoscientific and conspiracy endorsement completed a primed lexical decision task containing related prime-target words (e.g., bulb-light) and unrelated prime-target words (e.g., sock-light). Bayesian linear mixed-effects models over response times (RTs) revealed a main direct priming effect (faster RTs in related pairs than in unrelated ones), a main facilitatory effect for some EUB scores (i.e., the higher the value for EUB score, the faster RTs), and an interactive effect between the experimental manipulation and some EUB scores (the higher the EUB score, the smaller the direct priming effect). These results are consistent with predictions made from the enhanced spreading of activation explanatory mechanism, but other alternative accounts are also discussed.

Corporate internal control, capacity utilization and total factor productivity

Based on Internal Control (IC) theory and Principal-agent theory, this study explores the impacts of IC on capacity utilization and total factor productivity, and the internal mechanism among them. The results show that effective IC improves total factor productivity and capacity utilization. Sufficient capacity utilization has a mediating effect for the impact of IC and total factor productivity. Heterogeneity discussion shows that with higher environmental uncertainty, effective IC has a more significant marginal effect on total factor productivity and capacity utilization, and sufficient capacity utilization has a greater mediating effect between IC and total factor productivity. Finally, it is suggested that regulators guide enterprises to strengthen IC construction, to improve capacity utilization and total factor productivity. Enterprises facilitate the mechanism that effective IC improves capacity utilization, and increases total factor productivity. This study enriches the literature on IC enabling corporate operation, and has practical significance for shaping competitive advantages.

Mendelian randomization analysis to identify potential drug targets for osteoarthritis

Background Osteoarthritis (OA) is a prevalent chronic joint disease for which there is a lack of effective treatments. In this study, we used Mendelian randomization analysis to identify circulating proteins that are causally associated with OA-related traits, providing important insights into potential drug targets for OA. Method Causal associations between 1553 circulating proteins and five OA-related traits were assessed in large-scale two-sample MR analyses using Wald ratio or inverse variance weighting, and the results were corrected for Bonferroni. In addition, sensitivity analyses were performed to validate the reliability of the MR results, including reverse MR analysis and Steiger filtering to ensure the causal direction between circulating proteins and OA; Bayesian co-localization and phenotypic scanning were used to eliminate confounding effects and horizontal pleiotropy. External validation was performed to exclude incidental findings using novel plasma protein quantitative trait loci. Finally, the online analysis tool Enrichr was utilized to screen drugs and molecular docking was performed to predict binding modes and energies between proteins and drugs to identify the most stable and likely binding modes and drugs. Result Four proteins were ultimately found to be reliably and causally associated with three OA-related features: DNAJB12 and USP8 were associated with knee OA, IL12B with spinal OA, and RGMB with thumb OA. The ORs for the above proteins were 1.51 (95% CI, 1.26–1.81), 1.72 (95% CI, 1.42–2.08), 0.87 (95% CI, 0.81–0.92), and 0.59 (95% CI, 0.47–0.75), respectively. Drug-predicting small molecules (doxazosin, XEN 103, and montelukast) that simultaneously target three proteins, DNAJB12, USP8, and IL12B, docked well. Conclusion Based on our comprehensive analysis, we can draw the conclusion that there is a causal relationship between the genetic levels of DNAJB12, USP8, IL12B, and RGMB and the risk of respective OA.They may be potential options for OA screening and prevention in clinical practice. They can also serve as candidate molecules for future mechanism exploration and drug target selection.

Correction: The dynamic lives of osseous points from Late Palaeolithic/Early Mesolithic Doggerland: A detailed functional study of barbed and unbarbed points from the Dutch North Sea

A privacy-preserved horizontal federated learning for malignant glioma tumour detection using distributed data-silos

Malignant glioma is the uncontrollable growth of cells in the spinal cord and brain that look similar to the normal glial cells. The most essential part of the nervous system is glial cells, which support the brain’s functioning prominently. However, with the evolution of glioma, tumours form that invade healthy tissues in the brain, leading to neurological impairment, seizures, hormonal dysregulation, and venous thromboembolism. Medical tests, including medical resonance imaging (MRI), computed tomography (CT) scans, biopsy, and electroencephalograms are used for early detection of glioma. However, these tests are expensive and may cause irritation and allergic reactions due to ionizing radiation. The deep learning models are highly optimal for disease prediction, however, the challenge associated with it is the requirement for substantial memory and storage to amalgamate the patient’s information at a centralized location. Additionally, it also has patient data-privacy concerns leading to anonymous information generalization, regulatory compliance issues, and data leakage challenges. Therefore, in the proposed work, a distributed and privacy-preserved horizontal federated learning-based malignant glioma disease detection model has been developed by employing 5 and 10 different clients’ architectures in independent and identically distributed (IID) and non-IID distributions. Initially, for developing this model, the collection of the MRI scans of non-tumour and glioma tumours has been done, which are further pre-processed by performing data balancing and image resizing. The configuration and development of the pre-trained MobileNetV2 base model have been performed, which is then applied to the federated learning(FL) framework. The configurations of this model have been kept as 0.001, Adam, 32, 10, 10, FedAVG, and 10 for learning rate, optimizer, batch size, local epochs, global epochs, aggregation, and rounds, respectively. The proposed model has provided the most prominent accuracy with 5 clients’ architecture as 99.76% and 99.71% for IID and non-IID distributions, respectively. These outcomes demonstrate that the model is highly optimized and generalizes the improved outcomes when compared to the state-of-the-art models.

The Korean medicine for aging cohort (KoMAC) study: A protocol for a prospective, multicenter cohort study on healthy aging in the population entering old age in South Korea

Background South Korea is anticipated to enter a super-aged society by 2025, necessitating a focus on healthy aging. In Korean medicine (KM), aging and disease susceptibility are individual specific, emphasizing personalized treatments, and many Korean local governments have integrated KM services for elderly people into the public sector. However, there is a notable absence of research incorporating KM to treat older adults. Aim The proposed study aims to examine the comprehensive health profiles of individuals entering old age in rural and urban areas and explore the significant correlations between healthy aging and four key factors: biological, psychological, social, and KM-based phenotype factors. It will also establish a database and blood biobank, serving as a platform for future research to develop a traditional KM-based healthy aging model. Methods A multiple randomized controlled trial design will be adopted in this prospective, multicenter cohort study for the clinical investigation of the markers associated with KM-based healthy aging. The aim is to recruit 1,000 participants who are entering old age from both urban and rural settings for this study, and recruitment began in August 2023 with follow-up surveys planned at one-year intervals. Comprehensive health profiles, including biological, psychological, social, and KM-based phenotype factors, will be developed through the creation of a database, a blood biobank, and multi-omics data. Results In the baseline phase of this study, we will focus on identifying markers for KM-based phenotypes and examining how these phenotypes relate to aging and associated diseases. In the next phase, we will implement interventions tailored to KM-based phenotypes to verify the effects of KM on healthy aging. Ultimately, we intend to develop a KM-based integrated health management model, with further substudies aiming to explore factors related to healthy aging. This protocol was approved by the institutional review board of Wonkwang University Korean Medicine Hospital, Iksan, Republic of Korea (approval number: WKUIOMH-IRB-2023-05) on August 16, 2023 and Jangheung Integrative Medical Hospital (approval number: WKUJIM-202307-001) on August 21, 2023. Recruitment started on August 16, 2023. Conclusion The anticipated results of our study aim to establish personalized preventive and therapeutic interventions for individuals entering old age. Additionally, we seek to offer an KM-based integrated health management model that incorporates comprehensive diagnosis and an integrative medical treatment strategy for healthy aging. Trial Registration Clinical Research Information Service: KCT0008863 (registered on October 11, 2023, https://cris.nih.go.kr/cris/search/detailSearch.do/25718).

Ecological momentary assessment of physical and eating behaviours: The WEALTH feasibility and optimisation study with recommendations for large-scale data collection

Ecological Momentary Assessment (EMA) enables the real-time capture of health-related behaviours, their situational contexts, and associated subjective experiences. This study aimed to evaluate the feasibility of an EMA targeting physical and eating behaviours, optimise its protocol, and provide recommendations for future large-scale EMA data collections. The study involved 52 participants (age 31±9 years, 56% females) from Czechia, France, Germany, and Ireland completing a 9-day free-living EMA protocol using the HealthReact platform connected to a Fitbit tracker. The EMA protocol included time-based (7/day), event-based (up to 10/day), and self-initiated surveys, each containing 8 to 17 items assessing physical and eating behaviours and related contextual factors such as affective states, location, and company. Qualitative insights were gathered from post-EMA feedback interviews. Compliance was low (median 49%), particularly for event-based surveys (median 34%), and declined over time. Many participants were unable or unwilling to complete surveys in certain contexts (e.g., when with family), faced interference with their daily schedules, and encountered occasional technical issues, suggesting the need for thorough initial training, an individualised protocol, and systematic compliance monitoring. The number of event-based surveys was less than desired for the study, with a median of 2.4/day for sedentary events, when 4 were targeted, and 0.9/day for walking events, when 3 were targeted. Conducting simulations using participants’ Fitbit data allowed for optimising the triggering rules, achieving the desired median number of sedentary and walking surveys (3.9/day for both) in similar populations. Self-initiated reports of meals and drinks yielded more reports than those prompted in time-based and event-based EMA surveys, suggesting that self-initiated surveys might better reflect actual eating behaviours. This study highlights the importance of assessing feasibility and optimising EMA protocols to enhance subsequent compliance and data quality. Conducting pre-tests to refine protocols and procedures, including simulations using participants’ activity data for optimal event-based triggering rules, is crucial for successful large-scale data collection in EMA studies of physical and eating behaviours.

Application of 3D point cloud and visual-inertial data fusion in Robot dog autonomous navigation

The study proposes a multi-sensor localization and real-timeble mapping method based on the fusion of 3D LiDAR point clouds and visual-inertial data, which addresses the issue of decreased localization accuracy and mapping in complex environments that affect the autonomous navigation of robot dogs. Through the experiments conducted, the proposed method improved the overall localization accuracy by 42.85% compared to the tightly coupled LiDAR-inertial odometry method using smoothing and mapping. In addition, the method achieved lower mean absolute trajectory errors and root mean square errors compared to other algorithms evaluated on the urban navigation dataset. The highest root-mean-square error recorded was 2.72m in five sequences from a multi-modal multi-scene ground robot dataset, which was significantly lower than competing approaches. When applied to a real robot dog, the rotational error was reduced to 1.86°, and the localization error in GPS environments was 0.89m. Furthermore, the proposed approach closely followed the theoretical path, with the smallest average error not exceeding 0.12 m. Overall, the proposed technique effectively improves both autonomous navigation and mapping for robot dogs, significantly increasing their stability.

Comparative analysis of VMAT plans on Halcyon and infinity for lung cancer radiotherapy

Objective The dosimetric characteristics and treatment efficiency of VMAT plans using two linear accelerator platforms, Halcyon and Infinity, in conventional radiotherapy for non-small cell lung cancer (NSCLC) are compared to provide data for selecting clinical equipment. The study also explores potential confounding factors that may influence treatment outcomes. Methods This retrospective cohort study aims to compare the dosimetric characteristics and treatment efficiency of VMAT plans delivered using Halcyon and Infinity linear accelerator platforms in patients with NSCLC. A retrospective analysis was performed on 60 NSCLC patients receiving conventional fractionated radiotherapy with VMAT plans developed for both Halcyon and Infinity. These plans were optimized with RayStation 9A with identical dose constraints and optimization parameters. The groups were compared in terms of target dose coverage, normal tissue sparing, plan complexity, and treatment efficiency. The dosimetric parameters included D98%, D2%, and Dmean for both the CTV and PTV and dose distributions for organs at risk (OARs), including the heart, lungs, and spinal cord. Logistic regression was performed to account for potential confounding factors, such as PTV volume, tumor stage, and tumor location. Results The VMAT plans of both platforms met the clinical dosimetric requirements. Halcyon showed superior protection of normal tissues in low-dose areas (e.g., Lungs V5Gy and Heart V30Gy), whereas Infinity excelled in controlling hot spots and achieving rapid dose fall-off at the target margins. Furthermore, Halcyon has fewer plan monitoring units and lower complexity than Infinity and reduced treatment time by 24.0%. Logistic regression analysis revealed that PTV volume was a significant predictor for dose metric differences, while tumor stage and tumor location had variable effects depending on the dose metric, highlighting the need to account for these factors in clinical comparisons. Overall, there was no significant difference in target dose coverage or uniformity between the platforms; each demonstrated specific strengths in protecting different OARs and in treatment execution efficiency. Conclusion Halcyon and Infinity offer distinct advantages in radiotherapy for NSCLC. Halcyon provides better protection of normal tissues and performance in low-dose regions, whereas Infinity offers greater treatment efficiency and superior control in high-dose regions. The study also highlights that PTV volume is an important factor influencing dosimetric outcomes. In choosing optimal radiotherapy equipment in clinical practice, the study results suggest that treatment planning should leverage the unique technical features of different accelerators to achieve the best individualized outcomes. Future studies should increase the sample size and employ prospective research designs to confirm the clinical relevance of these findings.

Optimization study of mine fire sensor based on grey correlation analysis

In order to address the issues of fire alarm delay, omission, and false alarms caused by the current setup of mine fire sensors, which collect single disaster information, have fixed distribution, and relatively independent data collection, this study utilized FDS numerical simulation software and fire similarity experiments. The aim was to investigate the characteristics of fire gases, temperature, and wind speed. To optimize the number and location of fire sensors in mines, the mathematical method of grey correlation analysis was proposed. Additionally, the critical time for fire hazards to spread to other tunnels was determined by detecting the CO content of ventilation nodes in tunnels with different wind speeds. Grey correlation analysis was employed to determine the critical time for fire hazards to spread to other tunnels. The CO content of the ventilation node is measured to determine the time at which the fire hazard may spread to other tunnels. Grey correlation analysis is then used to compare and correlate the CO content of the ventilation node and the wind speed with the fire characteristic gases, wind speed, and temperature of the tunnel under different wind speeds. Additionally, taking into account the safe escape time for personnel suggested by Marchant, an optimization scheme for the sensors is proposed.

Predicting Type 2 diabetes onset age using machine learning: A case study in KSA

The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted at King Abdulaziz Medical University Hospital, aims to predict the age at onset of T2D using Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Regression (SVR), and Decision Tree Regression (DTR). It also seeks to identify key predictors influencing the age at onset of T2D in Saudi Arabia, which ranks 7th globally in prevalence. Medical records from 1,000 diabetic patients from 2018 to 2022 that contain demographic, lifestyle, and lipid profile data are used to develop the models. The average onset age was 65 years, with the most common onset range between 40 and 90 years. The MLR and RF models provided the best fit, achieving R2 values of 0.90 and 0.89, root mean square errors (RMSE) of 0.07 and 0.01, and mean absolute errors (MAE) of 0.05 and 0.13, respectively, using the logarithmic transformation of the onset age. Key factors influencing the age at onset included triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL), ferritin, body mass index (BMI), systolic blood pressure (SBP), white blood cell count (WBC), diet, and vitamin D levels. This study is the first in Saudi Arabia to employ MLR, ANN, RF, SVR, and DTR models to predict T2D onset age, providing valuable tools for healthcare practitioners to monitor and design intervention strategies aimed at reducing the impact of T2D in the region.